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2 El.Salvador_Min 34.0000000
3 Guatemala_Min 22.0000000
4 CPI_Min 23.0000000
5 Self.Harm_Min 0.1401138The effect of Corruption on Deaths of Despair
Introduction
The concept of “deaths of despair”— defined as fatalities due to suicides, alcohol-related diseases, and drug overdoses—poses a significant public health, economic, and societal challenge. In Honduras, where governance issues and socio-economic instability are prevalent, understanding the factors contributing to such deaths is crucial for developing effective policies and interventions. This research seeks to identify the relationship between perceived corruption and deaths of despair in Honduras.
Extensive research indicates that corruption adversely affects societies, primarily by undermining economic growth, reducing the efficacy of public institutions, and misallocating critical public resources. Articles from researchers such as Uslaner (2008) and Rothstein and Uslaner (2005) have recorded and studied how corruption exacerbates inequality, diminishes social trust, and hinders the delivery of public services, potentially leading to increased societal despair and health crises. Despite the well-documented impact of economic factors like unemployment and poverty on public health, as explored by Case and Deaton (2015), the specific link between corruption and deaths of despair remains ambiguous, especially within the context of Central American countries like Honduras.
Honduras is an ideal candidate for this study due to its high levels of perceived corruption and ongoing challenges in healthcare delivery. Pearson et al. (2012) highlights critical deficiencies in the Honduran healthcare system, noting that significant portions of the population live without health insurance or access to adequate healthcare. Further, Muñoz and Menna (2024) describes the healthcare system as plagued by underfunding, inefficiency, and resource shortages—conditions likely exacerbated by corrupt practices. Suggesting a potential correlation between corruption and adverse health outcomes, including deaths of despair.
Using multiple regression analysis to examine how perceived corruption might directly or indirectly influence deaths of despair rates, this research will be able to supply some answers on the topic. By controlling for variables such as GDP per capita, unemployment rates, healthcare expenditure, and demographic factors like education levels, the study seeks to isolate the effect of corruption on public health outcomes. Previous studies such as Botero-Rodrı́guez, Pantoja-Ruiz, and Rosselli (2023), Hanf et al. (2011), Alfano et al. (2022), and Kodila-Tedika, Kanyama-Kalonda, and Azia-Dimbu (2013) have established relationships between corruption and health outcomes. However, direct research on corruption’s impact on deaths of despair remains scarce, except for Yamamura, Andrés, and Katsaiti (2012) study on corruption and suicide. Yamamura, Andrés, and Katsaiti (2012) paper states, “Our results show that suicide rates are lower in countries with lower levels of corruption.” Meaning that there should be some relationship between the higher levels of corruption and potentially higher rates of deaths of despair.
The importance of this research extends beyond establishing a causal relationship; it holds practical implications for policymakers and health practitioners in Honduras and similar contexts. By identifying the relationship between corruption and deaths of despair, the findings could help guide targeted interventions designed to mitigate corruption’s impact on individuals and enhance health, economic, and social outcomes. Additionally, given the worsening Corruption Perception Index scores in Central American countries like Honduras, Guatemala, and El Salvador, understanding these relationships is increasingly urgent in order to inform policies and support those most in need.
Hypothesis
Higher perceived levels of corruption in Honduras are associated with increased rates of deaths of despair, specifically through mechanisms of reduced trust in public institutions and misallocation of resources critical to economic growth, as well as health and social services.
Theory
This hypothesis is based around the understanding that corruption greatly undermines trust in public institutions and prevents essential resources from reaching critical services like healthcare and social support systems. This is especially harmful in regions like Honduras, where public services are already under a lot of strain. According to Uslaner (2008) and Rothstein and Uslaner (2005), corruption creates inequalities and depletes the social capital needed for the health of society as a whole, thereby increasing the risk of despair.
Corruption’s link to deaths of despair is also observed through its impact on socio-economic stability and psychological health. Corruption aggravates economic inequalities and decreases future economic prospects for the general population. This perception, coupled with weakened healthcare and social support, increases risk factors for mental health issues and substance abuse—both recognized contributors to deaths of despair Case and Deaton (2015). These conditions create a sense of hopelessness and helplessness, both of which are significant drivers behind suicides and substance-related deaths.
Research also suggests that the detrimental impacts of corruption are not limited to immediate economic consequences but also affect long-term public perceptions of stability and well-being. Perceptions of a corrupt system with no recourse can increase societal despair, leading to poor health outcomes. Focusing on the specific context of Honduras, this paper intends to extend existing research by finding out how perceived corruption might directly correlate with, and potentially cause, an increase in deaths of despair. This is especially important for Honduras as, according to the CPI figures, it has the lowest CPI score in the last 20 years.
Research Design
Data Sets
For my primary corruption perception variable, I will be using Transparency International’s Corruption Perception Index (CPI). According to the webpage, it is “a combination of at least 3 data sources drawn from 13 different corruption surveys and assessments,” meaning that the index should be very reliable. I have seen its use by several researchers, such as Hanf, Alfano, and Botero-Rodriguez. I will also be using two other sources as supplemental data. This data runs from 2001 - 2023.
The primary data source for deaths of despair is the WHO Global Health estimates, which are country-level cause-specific mortality estimates for the years 2000, 2010, 2015, and 2019. According to the WHO, “These estimates are produced using data from multiple sources, including national vital registration data, latest estimates from WHO technical programs, United Nations partners and inter-agency groups, as well as the Global Burden of Disease and other scientific studies.” I will be using different metrics to measure deaths that are directly deaths of despair or could have resulted from excessive alcohol or drug consumption. All of the data is in deaths in (’000). Due to only having numbers from certain years I have used linear interpolation to identify potential values for years inbetween the established data points.
The third source of data is from The World Bank’s World Development Indicators (WDI), according to The World Bank it is “compiled from officially recognized international sources. It presents the most current and accurate global development data available,”. I will be using the total suicide mortality rate, the suicide mortality rate by gender, Educational attainment, healthcare expenditure, GDP per capita, the unemployment rate and the total Population from this dataframe as well primarily to compare with the WHO interpolation, and for covariate purposes.
Variables
The Corruption Perceptions Index (CPI) from Transparency International, which scales countries on a range from 0 (highly corrupt) to 100 (very clean), is a critical variable to understanding the levels of corrupton perception in a country over time. The CPI will be used as a measure of public sector corruption levels within a country, with the underlying theory suggesting that higher corruption may impede effective government services and public health initiatives, potentially leading to increased despair and risky behaviors among the local population.
Additionally, specific mortality outcomes from the World Health Organization’s Global Health Estimates have been chosen, including deaths due to self-harm (V203), alcohol use disorders (V115), drug use disorders (V116), liver cancer secondary to alcohol use (V76), and cirrhosis of the liver due to alcohol use (V157). These variables are indicative of “deaths of despair” and will be analyzed to determine if there is a correlation with the CPI scores. This approach is based on the premise that corruption could exacerbate conditions leading to these types of deaths by undermining health systems, social support structures, and economic stability. These values have been totaled up for the entire population, females and males.
To capture a broader perspective on mental health crises that might be influenced by socio-economic factors affected by corruption, additional variables from the World Bank’s World Development Indicators have been included. These are the suicide mortality rate per 100,000 population, along with gender-specific rates for males and females. These rates will help to explore whether corruption’s impact on public despair also shows variation between genders, potentially reflecting different social, economic, and cultural pressures experienced by males and females. GDP per capita is measured in current USD value. Unemployment is measured as a percentage of the total labor force. Health Expenditure is the total spending in current USD value. Education attainment is the percentage of adults that completed secondary education.
Table
Regression Model
To test my hypothesis that high perceived levels of corruption in Honduras are associated with increased rates of despair-related deaths, I will use a multiple linear regression analysis to estimate the influence of corruption perception on health outcomes.
My dependent variable is the Deaths of Despair Rate, measured as the annual number of deaths in the thousands attributed to suicide, alcohol-related diseases, and drug overdoses.
My primary independent variable is the Corruption Perception Index (CPI). This index measures the perceived levels of public sector corruption, scoring on a scale of 0 (highly corrupt) to 100 (very clean). Each country’s score combines at least three data sources drawn from 13 different corruption surveys and assessments. These data sources are collected by various reputable institutions, including the World Bank and the World Economic Forum.
Covariates & Confounders
The main covariates I will control are GDP per Capita, Unemployment Rate, Healthcare Expenditure per Capita, and Education level. The GDP per capita is a mechanism to control the country’s economic conditions, as economic downturns can create increased deaths of despair separate from corruption. The unemployment rate is to control for the overall economic well-being and financial stress of people on a more household level; low economic well-being and increased financial stress will also cause deaths of despair. Healthcare per capita is used to control the influence of healthcare access and quality on healthcare outcomes, as NGOs, foreign states, and other international organizations have the potential to increase healthcare expenditures drastically. At the same time, the local government might be corrupt and embezzling resources. I will control for Education level (% of adults attaining secondary education) to account for a potential impact on health awareness and financial stability.
Including these covariates is crucial for isolating the effect of corruption from other variables that also impact health outcomes. GDP per capita, unemployment rate, healthcare expenditure, education level, and urbanization rate are well-documented factors influencing public health and can confound the relationship between corruption and deaths of despair.
There are several obstacles and challenges to address/overcome when talking about the model. The first is Omitted Variable Bias; unobserved variables might affect corruption perception and deaths of despair that I have not controlled for. These include but are not limited to cultural factors, illegal drug trade intensity, or political stability. Another challenge is addressing reverse causality; it is possible that higher deaths of despair could cause increased perceptions of corruption as the public loses trust in their public institutions. However, I would assume, based on my experience, that this would only occur if the number of deaths of despair caused a national emergency, in which case the number of deaths would have to be magnitudes higher than they are now. The last challenge I would find very hard to overcome is measurement error, which is errors in measuring the corruption perception or the rates of deaths of despair. This measurement error could bias the results, but as the CPI is thoroughly reviewed and calculated on a multitude of measures from separate and reputable sources, I do not think measurement error is a significant consideration. However, in terms of deaths of despair, there is a real chance that, over time, the incidence of deaths of despair in Honduras has not changed. However, the ability for people to report deaths correctly and access healthcare information to report these deaths accurately may have changed. This might precisely be the cause of the increase in the deaths of despair between 2000-2015.
Empirical Extension
To improve the credibility of the analysis on the impact of perceived corruption on deaths of despair in Honduras, accounting for gender and taking a gender-specific approach could provide valuable insights and help rule out another potential confounder in the experience and reporting of deaths of despair.
Gender could significantly influence both the perception of corruption and the health outcomes associated with deaths of despair. Research has shown that men and women may experience and respond to despair differently due to biological, psychological, and social factors. Men are often more likely to die by suicide or alcohol-related causes, while women may experience higher rates of attempted suicide and depression. Gender differences in economic opportunities, social roles, and access to healthcare can also modify the impact of corruption on health outcomes. Additionally, in a paper by Entrup et al. (2023), he states that “men died in greater numbers and lost more years of life than women… the degree of gender disparity varied by cause of death. For example, men accounted for approximately 55% of COVID-19 deaths and YLL, but almost 80% of deaths and YLL from suicide. Additionally, men represented approximately 70% of deaths and YLL due to accidental drug overdose and alcohol-induced causes of death.” There is a definite possibility that corruption will have a differing effect on deaths of despair for each gender.
Findings
Plot of Deaths of Despair in the Thousands and CPI Over Time
Regression Model
| (1) | |
|---|---|
| (Intercept) | -1.144 |
| (0.253) | |
| CPI | 0.046 |
| (0.011) | |
| GDPpc | 0.001 |
| (0.000) | |
| Unemployment | 0.101 |
| (0.025) | |
| HealthEXpc | 0.001 |
| (0.002) | |
| SecondaryEdu | -0.018 |
| (0.023) | |
| Num.Obs. | 14 |
| R2 | 0.979 |
| R2 Adj. | 0.965 |
| AIC | -29.4 |
| BIC | -25.0 |
| Log.Lik. | 21.719 |
| RMSE | 0.05 |
Interpretation
The intercept value of -1.144, with a standard error of 0.253, suggests the baseline level of the deaths of despair rate when all independent variables are zero. The statistical significance of this value indicates that the baseline itself is significantly different from zero.
The coefficient for CPI is 0.046 with a standard error of 0.011. This positive coefficient indicates that an increase in the CPI score (suggesting lower perceived corruption) is associated with an increase in the rate of deaths of despair. This result is counter intuitive based on the hypothesis and suggests a need to reevaluate either the data or the presumed relationship.
The GDP per capita has a coefficient of 0.001, meaning that as the GDP per capita increases, there is a slight increase in deaths of despair. Given the very small magnitude of this coefficient and its standard error being less than the coefficient, the effect is minimal but statistically significant.
The coefficient for unemployment is 0.101 with a standard error of 0.025, indicating that higher unemployment rates are associated with higher rates of deaths of despair. This finding supports typical socioeconomic theories that link economic downturns to increased mental health issues and mortality.
The coefficient for Healthcare Expenditure per Capita is 0.001 with a standard error of 0.002. This suggests a very slight, nearly negligible positive relationship between healthcare spending per capita and deaths of despair, which is not statistically significant given the magnitude and error.
The coefficient for Secondary Education of -0.018 with a standard error of 0.023 indicates a non-significant relationship between higher levels of secondary education and deaths of despair, suggesting that increases in education or completion of secondary education does not significantly affect the rates of deaths of despair in the model.
With only 14 observations, the data set is quite small, which could impact the robustness and generalization of the findings. The R-squared value of 0.979 suggests that the model explains 97.9% of the variability in the dependent variable, which appears to be exceptionally high and may indicate a need to look at the model. At 0.965 the Adjusted R-squared slightly adjusts for the number of predictors in the model but still indicates a very high explanatory power. The AIC and BIC values are -29.4 and -25.0, respectively, indicating a good model fit relative to other potential models. The log likelihood of 21.719 suggests the likelihood of the data given the model is relatively high. An RMSE of 0.05 indicates that the model’s predictions are close to the observed data points, on average. Overall it appears that given the high R-squared and adjusted R-squared values reported, the model appears to explain a majority of the variance in the dependent variable. However, the small sample size could still undermine confidence in these results.
The results suggest that the variables GDP per capita and unemployment rate significantly impact the deaths of despair, aligning somewhat with economic theories. However, the positive coefficient on CPI is unexpected, suggesting that higher perceptions of corruption correlate with higher deaths of despair, contrary to the hypothesized relationship. This discrepancy could reflect issues such as reverse causality, omitted variable bias, or measurement errors in variables. More in-depth analysis and possibly additional data are needed to further explore and validate these results. An omitted/unobservable variable seems to likely be the culprit for this finding. It would make logical sense that as a country becomes less corrupt they are more likely to have more accurate reporting of nationwide statistics and people are more likely to trust national institutions and provide correct information. Which might mean that the actuals rates of deaths of despair did not increase, just the rate of reporting.
Empirical Extension Regression - Male
| (1) | |
|---|---|
| (Intercept) | -1.159 |
| (0.227) | |
| CPI | 0.045 |
| (0.010) | |
| GDPpc | 0.000 |
| (0.000) | |
| Unemployment | 0.089 |
| (0.023) | |
| HealthEXpc | 0.001 |
| (0.002) | |
| SecondaryEdu | -0.015 |
| (0.021) | |
| Num.Obs. | 14 |
| R2 | 0.971 |
| R2 Adj. | 0.953 |
| AIC | -32.5 |
| BIC | -28.0 |
| Log.Lik. | 23.228 |
| RMSE | 0.05 |
Interpretation
The model intercept is -1.159, with a standard error of 0.227. This indicates the expected level of deaths of despair when all predictors are held at zero, which, given the context, acts as a statistical control.
The coefficient for CPI is 0.045 with a standard error of 0.010, suggesting a statistically significant positive relationship between corruption perception and deaths of despair among males. For every unit increase in the CPI score, indicating a perception of decreased corruption, deaths of despair increase by 0.045 per 100,000 male population.
The GDP per capita coefficient is very small (0.000) with its standard error also at 0.000, indicating that changes in GDP per capita have a negligible effect on deaths of despair among males in this sample.
The unemployment coefficient is 0.089 with a standard error of 0.023, showing that an increase in the unemployment rate is associated with an increase in deaths of despair. This finding underscores the economic determinants of mental health and risk behaviors leading to despair.
The Healthcare Expenditure per capita coefficient of 0.001 with a standard error of 0.002 suggests a small and statistically non-significant effect of healthcare expenditure on deaths of despair among males.
The coefficient for secondary education is -0.015 with a standard error of 0.021, suggesting a non-significant and slightly negative association with deaths of despair.
The model was run with 14 observations, indicating a relatively small sample size that might affect the generalization of the results. The model’s R-squared is 0.971 and the adjusted R-squared is 0.953, indicating that the model explains a very high proportion of the variance in deaths of despair among males from the predictors used. The AIC (-32.5) and BIC (-28.0) suggest good model fit relative to possible alternative models. The log-likelihood value is 23.228, indicating the likelihood of the data under the model. An RMSE of 0.05 shows that the model predictions are closely aligned with the observed data, indicating good model accuracy. The significant positive relationship between CPI and deaths of despair among males might suggest that perceptions of corruption—or perhaps the stress and instability associated with such perceptions—might have a pronounced psychological or social impact on males in Honduras. However, the impact of variables like healthcare expenditure and secondary education levels could indicate that these factors alone are not sufficient to mitigate the risk of deaths of despair without addressing broader socio-economic and governance issues.
The results should be interpreted with caution due to the small sample size and the high R-squared values. Further research with a larger dataset would be valuable to confirm these findings and to explore additional variables that might influence the relationship between corruption perception and health outcomes in different demographics.
Empirical Extension Regression - Female
| (1) | |
|---|---|
| (Intercept) | 0.015 |
| (0.041) | |
| CPI | 0.001 |
| (0.002) | |
| GDPpc | 0.000 |
| (0.000) | |
| Unemployment | 0.012 |
| (0.004) | |
| HealthEXpc | -0.001 |
| (0.000) | |
| SecondaryEdu | -0.003 |
| (0.004) | |
| Num.Obs. | 14 |
| R2 | 0.990 |
| R2 Adj. | 0.984 |
| AIC | -80.6 |
| BIC | -76.1 |
| Log.Lik. | 47.299 |
| RMSE | 0.01 |
Interpretation
The intercept is relatively small at 0.015 with a standard error of 0.041, indicating that when all predictors are zero, the deaths of despair rate is essentially negligible. This suggests that other factors, rather than the baseline level, are more influential in this model.
The coefficient for CPI is 0.001 with a standard error of 0.002, indicating a very small and statistically non-significant impact of perceived corruption on deaths of despair among females. This suggests that for females, the perception of corruption might not be a strong predictor of despair-related outcomes.
The GDP per capita coefficient is effectively zero (0.000) with a negligible standard error. This indicates no impact of economic output per capita on deaths of despair among females in this dataset.
The unemployment rate has a small but statistically significant coefficient of 0.012 with a standard error of 0.004. This suggests that increases in unemployment have a slight but noticeable effect on increasing deaths of despair among females.
The Healthcare Expenditure per Capita coefficient is -0.001 with a very small standard error, suggesting a minimal but significant negative relationship. This indicates that higher healthcare spending per capita is associated with a slight decrease in deaths of despair among females.
The Secondary Education coefficient of -0.003 with a standard error of 0.004 shows a non-significant, minimal negative association between higher levels of secondary education and deaths of despair among females.
Similar to the male model, this model also uses 14 observations. The small sample size could limit the generalization of the findings.The model’s R-squared is exceptionally high at 0.990 with an adjusted R-squared of 0.984, indicating that the model explains nearly all the variance in the dependent variable for females. This like the other models could suggest an overfitting issue, especially given the small sample size. The AIC (-80.6) and BIC (-76.1) are extremely low, further indicating a potentially overfit model. A high log-likelihood value of 47.299 suggests that the model fits the data very well under the assumed linear framework. An RMSE of 0.01 indicates very close alignment between the model’s predictions and the observed data, highlighting high model accuracy.
The results indicate that socio-economic factors like unemployment may have a slightly higher impact on deaths of despair among females than perceived corruption or GDP per capita. This could suggest that economic stability and access to healthcare are critical areas for interventions aimed at reducing despair among women in Honduras. The negligible impact of corruption, as opposed to the significant effects seen in the male-focused model, may point to different underlying causes of despair between genders that need to be addressed separately in public health policies.
The findings should be approached with caution due to the potential overfitting suggested by the very high R-squared values and the small sample size, which might affect the reliability of the results. Further research with larger, more diverse sample, additional variables, or a standardized deaths of despair variable could provide more definitive insights and help to develop gender-specific strategies to combat deaths of despair in Honduras.
Discussion and Policy Implications
My final project observing the relationship between perceived corruption and deaths of despair in Honduras, resulted in several key insights and lessons.The first key insight was with regards to the gender based differences. The results indicate a significant difference in how perceived corruption impacts deaths of despair between males and females. For males, there was a positive correlation between the Corruption Perception Index (CPI) and deaths of despair, suggesting that higher perceived corruption is associated with an increase in such deaths. For females, however, the results showed a negligible impact of corruption on deaths of despair. Secondly, both models indicated that economic factors like unemployment have a noticeable effect on deaths of despair. In the female model, even small changes in unemployment were significant, showing household economic stability as a crucial factor. Thirdly, there were interesting Healthcare expenditure results. For females, higher healthcare expenditure was associated with a slight decrease in deaths of despair, indicating the potential protective effect of better healthcare access and quality.
With regards to the strength or robustness of the evidence, the evidence for males suggests a strong positive association between perceived corruption and deaths of despair, supporting the hypothesis for this subgroup. However, for females, the evidence does not support the hypothesis as the association was negligible. The high R-squared values, particularly in the female model, suggest that while the models explain a large proportion of variance, there could be over fitting due to the small sample size. This is concerning especially with regards to the generalization and robustness of the findings.
If these findings are substantiated by further research, unique policy interventions could be created to address gender-specific needs. For males, efforts to reduce corruption could be directly linked to improving mental health outcomes. For females, enhancing economic stability and healthcare access might be more effective. Policies aimed at reducing unemployment and improving economic conditions could have broad benefits in reducing deaths of despair across genders.
For future research efforts there are several aspects that could be improved on. The first is obtaining a larger sample size. To increase the robustness and generalization of the results, future studies should include a larger sample size that allows for more reliable statistical analysis. The second is incorporating Longitudinal Data. Using longitudinal data could help establish causality more definitively by tracking changes over time in corruption perception and their impact on health outcomes. The next consideration is obtaining broader Socio-Economic Variables. Including additional socio-economic variables such as income inequality, variables related to social support networks, and regional economic conditions might provide a more comprehensive understanding of the factors contributing to deaths of despair. Lastly, qualitative studies could explore the personal experiences of individuals in Honduras to better understand how corruption impacts their lives and contributes to despair.
While the findings possess interesting initial insights, particularly for policy development targeting gender-specific needs, the results are not very robust and need to be approached with caution. Further research using larger data sets with more historic data, or potentially combining indicators from multiple sources could provide stronger evidence and help refine the implications for policy and intervention strategies.
References
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Botero-Rodrı́guez, Felipe, Camila Pantoja-Ruiz, and Diego Rosselli. 2023. “Corruption and Its Relation to Prevalence and Death Due to Noncommunicable Diseases and Risk Factors: A Global Perspective.” Revista Panamericana de Salud Pública 46: e10.
Case, Anne, and Angus Deaton. 2015. “Rising Morbidity and Mortality in Midlife Among White Non-Hispanic Americans in the 21st Century.” Proceedings of the National Academy of Sciences 112 (49): 15078–83.
Entrup, Parker, Leon Brodsky, Candice Trimble, Stephanie Garcia, Nasra Mohamed, Megan Deaner, JP Martell, et al. 2023. “Years of Life Lost Due to Deaths of Despair and COVID-19 in the United States in 2020: Patterns of Excess Mortality by Gender, Race and Ethnicity.” International Journal for Equity in Health 22 (1): 161.
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Muñoz, César, and Santiago Menna. 2024. “Honduras Moves at a Snail’s Pace to Create an International Anticorruption Commission Human Rights Watch.” https://www.hrw.org/news/2024/01/02/honduras-moves-snails-pace-create-international-anticorruption-commission.
Pearson, Catherine A, Michael P Stevens, Kakotan Sanogo, and Gonzalo ML Bearman. 2012. “Access and Barriers to Healthcare Vary Among Three Neighboring Communities in Northern Honduras.” International Journal of Family Medicine 2012.
Rothstein, Bo, and Eric M Uslaner. 2005. “All for All: Equality, Corruption, and Social Trust.” World Politics 58 (1): 41–72.
Uslaner, Eric M. 2008. Corruption, Inequality, and the Rule of Law: The Bulging Pocket Makes the Easy Life. Cambridge University Press.
Yamamura, Eiji, Antonio R Andrés, and Marina Selini Katsaiti. 2012. “Does Corruption Affect Suicide? Econometric Evidence from OECD Countries.” Atlantic Economic Journal 40: 133–45.